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Int. J. Environ. Res. Public Health 2016, 13(5), 509; doi:10.3390/ijerph13050509

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

1
Independent Consultant, Paris 75006, France
2
YGM Consult, CEO, Paris 75015, France
3
School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
4
Institute Louis Bachelier, Paris 75002, France
5
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC 20057, USA
6
CGStat, CEO, Raleigh, NC 27607, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Igor Burstyn
Received: 29 February 2016 / Revised: 11 May 2016 / Accepted: 12 May 2016 / Published: 18 May 2016
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
View Full-Text   |   Download PDF [3226 KB, uploaded 19 May 2016]   |  

Abstract

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF) can be used to perform the clustering. By its nature, NMF-based clustering is focused on the large values. If the data is normalized by subtracting the row/column means, it becomes of mixed signs and the original NMF cannot be used. Our idea is to split and then concatenate the positive and negative parts of the matrix, after taking the absolute value of the negative elements. NMF applied to the concatenated data, which we call PosNegNMF, offers the advantages of the original NMF approach, while giving equal weight to large and small values. We use two public health datasets to illustrate the new method and compare it with alternative clustering methods, such as K-means and clustering methods based on the Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). With the exception of situations where a reasonably accurate factorization can be achieved using the first SVD component, we recommend that the epidemiologists and environmental scientists use the new method to obtain clusters with improved quality and interpretability. View Full-Text
Keywords: SVD; PCA; NMF; K-means SVD; PCA; NMF; K-means
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fogel, P.; Gaston-Mathé, Y.; Hawkins, D.; Fogel, F.; Luta, G.; Young, S.S. Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health. Int. J. Environ. Res. Public Health 2016, 13, 509.

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